Projects
Vishnu Priya Neerukattu has worked on a wide range of projects, each one unique and impactful.
Capstone Project – EV Population Forecasting
This capstone project forecasted electric vehicle (EV) adoption trends in Washington State from 2026 to 2030 using data science techniques. Utilizing historical registration data and models like XGBoost and Prophet, we predicted future EV growth and identified market leaders such as Tesla. The project produced actionable insights for infrastructure planning, including expansion of charging stations and state incentives, and emphasized the use of time-series forecasting for sustainable transportation policies.
COVID-19 Vaccination Data Analysis
This project analyzed global COVID-19 vaccination trends across continents and countries, highlighting disparities in access and coverage. Using visualizations created in Tableau, the study examined metrics such as people vaccinated per hundred, total doses administered, and fully vaccinated rates. The analysis helped to uncover vaccination gaps in low-income regions and provided comparative insights across major economies to guide public health strategies.
H1B Visa Dataset Analysis – Data Visualization Project
This project explored the U.S. H1B visa system using a Kaggle dataset of over 600,000 applications. Through Tableau visualizations and statistical analysis, we investigated factors influencing visa approvals such as employer state, job industry, processing time, and employment type. The study provided insights into top job roles and employers, approval trends by geography, and the impact of full-time vs part-time positions, assisting international professionals in navigating the H1B process.
Netflix Movies and Shows – SQL & Tableau Analysis
This project used SQL and Tableau to analyze over 80,000 records from Netflix’s shows and movies dataset. Key findings included the top and bottom-ranked titles by IMDb score, content distribution across decades, and most common genres. Genre analysis revealed comedy and documentary as top categories. The project culminated in a dynamic Tableau dashboard that provides interactive insights into content trends, aiding content strategy and viewer recommendations.
San Francisco Salaries – Business Decision Process Project
Using a public dataset on San Francisco government employee salaries (2012–2014), this project applied statistical methods like normal distribution, confidence intervals, hypothesis testing, and regression analysis. The study identified salary trends, tested mean salary hypotheses, and built a multiple regression model to predict total compensation. The analysis offered practical insights into salary distribution and predictors of total earnings among city employees.
Restaurant Simulation – Statistical Modeling Project
This simulation project determined the optimal number of chefs required in a restaurant meal delivery system to reduce lost orders and driver over-utilization. Using a discrete event simulation, the study found that hiring two chefs minimized driver utilization below 65% while drastically lowering lost orders. The analysis provided strategic recommendations for staffing and operational efficiency using simulation modeling.
World Bank Data Analysis – Global Trend Forecasting
This data mining project examined global trends in GDP, education, population growth, and CO2 emissions using World Bank datasets. Exploratory and predictive analyses addressed questions like forecasting GDP and predicting drops in life expectancy. A Random Forest model achieved high accuracy in identifying health risks, while linear regression explored GDP predictors. The project offers policymakers actionable insights into global development, economic disparities, and sustainability challenges.
